CV4MS @ CVPR 2024

Event: CVPR 2024 Workshop · Duration: 281 min · ▶ Watch on YouTube

Abstract

This segment features two talks from the CVPR 2024 Workshop on Computer Vision for Material Science. The first speaker, Elizabeth A. Holm, discusses data-frugal computer vision for microstructural science, emphasizing the use of transfer learning and active learning to achieve high performance with minimal annotated data. The second speaker, Luther W. McDonald IV, presents on morphological signatures of the nuclear fuel cycle, focusing on using image analysis to determine the processing history of uranium oxide materials for nuclear forensics. He highlights the challenges of working with out-of-distribution data and the importance of collaboration between experimentalists and data scientists. This segment features several talks and Q&A sessions from the CVPR 2024 Workshop, focusing on advanced computer vision techniques in materials science. Discussions cover the challenges of microstructure analysis, including segmentation, the impact of material impurities, and the need for standardized data. Presentations introduce novel deep learning architectures like VoIRAFT for 3D displacement field estimation in bone-implant interfaces and self-supervised learning with GANs for electron microscopy. The application of the Segment Anything Model (SAM) for grain characterization in hard drive design is explored, alongside the use of Vision Transformers (DinoV2) for learning microstructure-property relationships in materials. This segment features two talks from the CVPR 2024 Workshop. The first speaker introduces the concept of promptable vision foundation models for general-purpose vision, aiming to unify diverse tasks and modalities. The second speaker, Joshua Stuckner from NASA, presents MicroNet, a set of microstructure-aware pre-trained encoders for microscopy image analysis. He demonstrates how these in-domain encoders improve performance in tasks like semantic segmentation and property prediction, especially with limited training data and for out-of-domain generalization, facilitating automated materials design and discovery. The discussion also highlights the need for large, open-source microscopy datasets to further advance foundation models. This segment outlines a roadmap towards general-purpose vision foundations, emphasizing multimodal vision, multi-task unified models, and promptable interfaces. It details Microsoft Research’s contributions, including UniCL for unified contrastive learning in image-text-label space, GLIP for grounded language-image pretraining at the region level, and X-Decoder for generalized decoding across pixel, image, and language tasks. The presentation culminates with SEEM and Semantic-SAM for highly flexible and controllable segmentation, and BiomedParse, a biomedical foundation model, showcasing the application of these principles to specialized domains.

Speakers

  • Elizabeth A. Holm — University of Michigan
  • Luther W. McDonald IV — University of Utah
  • Luther McDonald — The University of Utah
  • Dr. Tak Ming Wong — Helmholtz-Zentrum Hereon
  • Bashir Kazimi — Forschungszentrum Jülich
  • Kai Nichols — Seagate Research Group
  • Sheila Whitman — University of Arizona
  • Jianwei Yang — Microsoft Research
  • Joshua Stuckner — Materials and Structures Division, NASA Glenn Research Center, Cleveland, OH
  • Jianfeng Gao — Microsoft Research

Talks (11)

  • 00:02:45 — Elizabeth A. Holm: Quality over quantity: Data frugal computer vision for microstructural science
    • This talk explores how data-frugal computer vision techniques, particularly transfer learning and active learning, can be effectively applied to microstructural science despite limited annotated data.
  • 00:39:30 — Luther W. McDonald IV: Morphological Signatures of the Nuclear Fuel Cycle
    • This presentation discusses the use of material morphology and image analysis to discern the processing history of uranium oxide materials for nuclear forensics, highlighting challenges with out-of-distribution data and the need for collaborative research.
  • 01:10:21 — Luther McDonald: Thank You!
    • Q&A session discussing the challenges of segmentation in microstructure analysis, the impact of impurities on morphology, the need for standardized materials for generalizability across instruments, and the application of deep learning for automated segmentation and classification.
  • 01:14:56 — Dr. Tak Ming Wong: VoIRAFT: Volumetric Optical Flow Network for Digital Volume Correlation of Synchrotron Radiation-based Micro-CT Images of Bone-Implant Interfaces
    • Presentation on VoIRAFT, a 3D optical flow network extending RAFT, for Digital Volume Correlation (DVC) in analyzing bone-implant interfaces from micro-CT images, demonstrating its effectiveness in estimating displacement fields in bio-degradable implants.
  • 01:21:01 — Bashir Kazimi: Self Supervised Learning with Generative Adversarial Networks for Electron Microscopy
    • Discussion on using self-supervised learning with Generative Adversarial Networks (GANs) to overcome the lack of annotated datasets in Transmission Electron Microscopy (TEM) for tasks like semantic segmentation, denoising, and super-resolution.
  • 01:26:36 — Kai Nichols: Segment Anything Model for Grain Characterization in Hard Drive Design
    • Exploration of the Segment Anything Model (SAM) for grain characterization in Heat-Assisted Magnetic Recording (HAMR) hard drive design, addressing challenges like poor boundary contrast, diverse application areas, and rapidly changing product development environments.
  • 01:33:01 — Sheila Whitman: Learning microstructure-property relationships in materials with robust features from vision transformers
    • Presentation on leveraging foundational models like DinoV2 (Vision Transformers) for extracting robust features from microstructure images to predict material properties, offering an alternative to traditional statistical or CNN-based approaches.
  • 03:31:04 — Jianfeng Gao: Towards General-Purpose Vision Foundation
    • An overview of the roadmap towards general-purpose vision foundation models, focusing on multimodal vision, multi-task unified models, and promptable interfaces.
  • 03:37:01 — Jianfeng Gao: Promptable Interface
    • Presentation of SEEM and Semantic-SAM for highly flexible and controllable segmentation, supporting various prompt types.
  • 04:05:33 — Jianwei Yang: Promtable Vision Foundation in the Wild: From Head to Tail
    • Introduces the concept of promptable vision foundation models and their application across various vision tasks, aiming for general-purpose vision foundation.
  • 04:20:57 — Joshua Stuckner: Enhancing Microscopy Image Analysis with In-Domain Pre-trained Encoders
    • Discusses the development and application of microstructure-aware pre-trained encoders (MicroNet) for automated analysis and quantification of microscopy images in materials science, demonstrating improved performance with limited data.

Key Takeaways

  • Deep learning, especially with transfer learning, can achieve high accuracy in microstructural image analysis tasks (segmentation, classification, regression) even with very limited annotated data.
  • Microstructural images are ‘data-rich’ compared to natural images, allowing for effective training with fewer samples.
  • Metrics like AMRD can optimize the selection of training images to maximize representativeness and diversity, leading to better model performance in low-data regimes.
  • Active learning approaches like PixelPick can significantly reduce annotation burden by iteratively querying pixel labels based on model uncertainty.
  • Morphology (microstructure) can serve as a unique signature for determining the processing history of nuclear materials, which is crucial for nuclear forensics.
  • Generalizing deep learning models to out-of-distribution microstructural data is a significant challenge, requiring robust methods and careful consideration of factors like imaging conditions, material aging, and impurities.
  • Deep learning, particularly self-supervised learning with GANs, offers promising solutions for automating labor-intensive tasks like segmentation and overcoming the lack of annotated datasets in materials science imaging.
  • Foundational models like DinoV2 and extensions of optical flow networks (e.g., VoIRAFT) can extract robust, task-agnostic features from complex material microstructures, enabling accurate property prediction and displacement field estimation.
  • The Segment Anything Model (SAM) shows potential for zero-shot segmentation in materials science, but challenges remain in handling small grains, low contrast edges, and rapidly changing product development environments.
  • Standardization of material image datasets and the development of robust pre-processing and post-processing techniques are crucial for improving the generalizability and reliability of deep learning models across different instruments and applications in materials science.
  • Pre-training vision models on domain-specific data, like microscopy images, significantly improves performance for downstream tasks compared to models pre-trained on general datasets like ImageNet, especially with limited labeled data.
  • Microstructure-aware pre-trained encoders (MicroNet) enable higher accuracy and robustness in microscopy image analysis, crucial for understanding material properties and accelerating inverse design processes.
  • The use of pre-trained encoders allows for efficient transfer learning, reducing the need for extensive labeled data in specialized domains like materials science.
  • There is a strong need for large, open-source microscopy datasets to further advance the development of robust foundation models and vision transformers for scientific applications.
  • The development of general-purpose vision foundation models is progressing through stages: from task-specific to pretrained, then unified, and finally general-purpose systems, mirroring trends in language models.
  • Bridging vision and language at different granularities (image-level, region-level, pixel-level) is crucial for building robust and versatile vision models capable of zero-shot transfer and open-vocabulary understanding.
  • Promptable interfaces, supporting various types of prompts (text, spatial, visual), enhance human-AI interaction and allow models to capture user intent more effectively across diverse tasks and domains.
  • Foundation models like BiomedParse demonstrate the successful application of general-purpose vision principles to specialized fields, enabling advanced capabilities in areas like medical image parsing.

Methods / Models / Datasets Mentioned

  • ALIGN
  • Additively Maximizing Representativeness and Diversity (AMRD) metric
  • AlexNet
  • Automatic Mask Generator (AMG)
  • BERT
  • BiomedParse
  • CIFAR-10
  • CLIP
  • ChatGPT
  • Claude
  • Coca
  • Convolutional Neural Network
  • Convolutional Neural Networks
  • DETR
  • DINO
  • DeepLabV3+
  • DinoV2
  • DyHead-T
  • FPN
  • Faster R-CNN
  • Flamingo
  • GIT
  • GLIP
  • GPR
  • GPT
  • GPT-4
  • GPT-4V
  • Gemini
  • Grad-CAM
  • ImageNet
  • Inception v4
  • LLaMa
  • LLaVA
  • Linear Support Vector Regression
  • LinkNet
  • MAMA Software
  • MNIST
  • Mask2former
  • MaskDINO
  • MedSAM
  • MicroNet
  • MoCo
  • Non-maximum suppression (NMS)
  • Ordinary Least Squares regression
  • PAN
  • PCA
  • PSPNet
  • PaLM
  • PixelPick
  • Poisson disk sampling
  • Polynomial Regression
  • RAFT
  • Random Forest Classifiers
  • RegionCLIP
  • ResNeXt50
  • ResNet
  • ResNet-34
  • ResNet50
  • SAM
  • SE-ResNeXt50
  • SEEM
  • Semantic-SAM
  • SimCLR
  • UMAP
  • UNet
  • UNet++
  • UniCL
  • VGG
  • VGG16
  • VGG16-BN
  • VLAD
  • ViT-h
  • VoIRAFT
  • X-Decoder

Topics

Active Learning · Biomedical imaging · Computer Vision · Computer Vision for Material Science · Contrastive learning · Data Frugal Learning · Data-driven methods · Deep Learning · Digital Volume Correlation (DVC) · Explainable AI · Foundation models · General-purpose vision · Generalizability · Generative Adversarial Networks (GANs) · Grain Characterization · Heat-Assisted Magnetic Recording (HAMR) · Image Segmentation · Inverse Design of Materials · Language-image pretraining · Materials Science · Microscopy Image Analysis · Microstructural Science · Microstructure Analysis · Microstructure Quantification · Multi-task unified models · Multimodal Vision · Multimodal vision · Nuclear Forensics · Out-of-Distribution (OOD) Data · Out-of-Domain Generalization · Pre-trained Encoders · Promptable interfaces · Segmentation · Self-Supervised Learning · Semantic Segmentation · Transfer Learning · Transmission Electron Microscopy (TEM) · Uranium Oxide Morphology · Vision Foundation Models · Vision Transformers

Notes

Open for commentary — connections to other work, critiques, follow-up reading.